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Using Inductive Learning To Generate Rules for Semantic Query Optimization (1995)
| Content Provider | CiteSeerX |
|---|---|
| Author | Knoblock, Craig A. Hsu, Chun-Nan |
| Abstract | Semantic query optimization can dramatically speed up database query answering by knowledge intensive reformulation. But the problem of how to learn the required semantic rules has not been previously solved. This chapter presents a learning approach to solving this problem. In our approach, the learning is triggered by user queries. Then the system uses an inductive learning algorithm to generate semantic rules. This inductive learning algorithm can automatically select useful join paths and attributes to construct rules from a database with many relations. The learned semantic rules are effective for optimization because they will match query patterns and reflect data regularities. Experimental results show that this approach learns sufficient rules for optimization that produces a substantial cost reduction. 17.1 Introduction This chapter presents an approach to learning semantic knowledge for semantic query optimization (SQO). SQO optimizes a query by using semantic rules, such as... |
| File Format | |
| Publisher Date | 1995-01-01 |
| Access Restriction | Open |
| Subject Keyword | Inductive Learning Algorithm Semantic Query Optimization Required Semantic Rule Learned Semantic Rule Database Query Answering Generate Rule Substantial Cost Reduction Semantic Rule Many Relation Knowledge Intensive Reformulation Data Regularity Useful Join Path Sufficient Rule Query Pattern |
| Content Type | Text |
| Resource Type | Article |
